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     ]
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 971,loss 69687.140625\n",
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     ]
    },
    {
     "name": "stdout",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1826,loss 12488.115234\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2415,loss 4266.188965\n",
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      "epoch 2855,loss 1768.303345\n",
      "epoch 2856,loss 1762.782959\n",
      "epoch 2857,loss 1759.113892\n",
      "epoch 2858,loss 1755.350952\n",
      "epoch 2859,loss 1752.930420\n",
      "epoch 2860,loss 1745.394287\n",
      "epoch 2861,loss 1739.225342\n",
      "epoch 2862,loss 1741.157227\n",
      "epoch 2863,loss 1738.381104\n",
      "epoch 2864,loss 1729.526611\n",
      "epoch 2865,loss 1727.261719\n",
      "epoch 2866,loss 1719.114502\n",
      "epoch 2867,loss 1715.115234\n",
      "epoch 2868,loss 1713.503540\n",
      "epoch 2869,loss 1707.078369\n",
      "epoch 2870,loss 1704.417969\n",
      "epoch 2871,loss 1697.438721\n",
      "epoch 2872,loss 1693.638062\n",
      "epoch 2873,loss 1692.751343\n",
      "epoch 2874,loss 1688.725220\n",
      "epoch 2875,loss 1683.937134\n",
      "epoch 2876,loss 1682.780029\n",
      "epoch 2877,loss 1672.928955\n",
      "epoch 2878,loss 1675.725830\n",
      "epoch 2879,loss 1667.520752\n",
      "epoch 2880,loss 1664.606201\n",
      "epoch 2881,loss 1660.974365\n",
      "epoch 2882,loss 1652.441040\n",
      "epoch 2883,loss 1650.053345\n",
      "epoch 2884,loss 1647.409546\n",
      "epoch 2885,loss 1640.967773\n",
      "epoch 2886,loss 1636.192871\n",
      "epoch 2887,loss 1633.350220\n",
      "epoch 2888,loss 1634.296021\n",
      "epoch 2889,loss 1623.532593\n",
      "epoch 2890,loss 1620.634277\n",
      "epoch 2891,loss 1618.473389\n",
      "epoch 2892,loss 1613.253906\n",
      "epoch 2893,loss 1609.214111\n",
      "epoch 2894,loss 1617.394775\n",
      "epoch 2895,loss 1604.139160\n",
      "epoch 2896,loss 1601.390503\n",
      "epoch 2897,loss 1598.790039\n",
      "epoch 2898,loss 1586.227661\n",
      "epoch 2899,loss 1590.463379\n",
      "epoch 2900,loss 1584.262451\n",
      "epoch 2901,loss 1581.860840\n",
      "epoch 2902,loss 1572.789062\n",
      "epoch 2903,loss 1567.962158\n",
      "epoch 2904,loss 1564.757812\n",
      "epoch 2905,loss 1563.042969\n",
      "epoch 2906,loss 1556.213135\n",
      "epoch 2907,loss 1553.410767\n",
      "epoch 2908,loss 1553.216064\n",
      "epoch 2909,loss 1559.653564\n",
      "epoch 2910,loss 1544.956299\n",
      "epoch 2911,loss 1541.724487\n",
      "epoch 2912,loss 1533.881714\n",
      "epoch 2913,loss 1527.825073\n",
      "epoch 2914,loss 1521.959473\n",
      "epoch 2915,loss 1520.186646\n",
      "epoch 2916,loss 1515.353638\n",
      "epoch 2917,loss 1511.479248\n",
      "epoch 2918,loss 1508.498413\n",
      "epoch 2919,loss 1501.070557\n",
      "epoch 2920,loss 1506.335938\n",
      "epoch 2921,loss 1499.957275\n",
      "epoch 2922,loss 1496.091553\n",
      "epoch 2923,loss 1487.460449\n",
      "epoch 2924,loss 1482.436523\n",
      "epoch 2925,loss 1486.376465\n",
      "epoch 2926,loss 1479.794678\n",
      "epoch 2927,loss 1471.777588\n",
      "epoch 2928,loss 1470.618408\n",
      "epoch 2929,loss 1463.766357\n",
      "epoch 2930,loss 1465.911377\n",
      "epoch 2931,loss 1472.672241\n",
      "epoch 2932,loss 1465.750244\n",
      "epoch 2933,loss 1457.284058\n",
      "epoch 2934,loss 1444.058960\n",
      "epoch 2935,loss 1444.742798\n",
      "epoch 2936,loss 1436.975830\n",
      "epoch 2937,loss 1432.068359\n",
      "epoch 2938,loss 1432.741577\n",
      "epoch 2939,loss 1425.095947\n",
      "epoch 2940,loss 1423.752197\n",
      "epoch 2941,loss 1419.187988\n",
      "epoch 2942,loss 1412.090820\n",
      "epoch 2943,loss 1411.369629\n",
      "epoch 2944,loss 1405.639038\n",
      "epoch 2945,loss 1400.395264\n",
      "epoch 2946,loss 1399.899170\n",
      "epoch 2947,loss 1393.795166\n",
      "epoch 2948,loss 1390.481445\n",
      "epoch 2949,loss 1391.976318\n",
      "epoch 2950,loss 1383.258423\n",
      "epoch 2951,loss 1384.877441\n",
      "epoch 2952,loss 1377.455322\n",
      "epoch 2953,loss 1374.163086\n",
      "epoch 2954,loss 1370.626221\n",
      "epoch 2955,loss 1365.583374\n",
      "epoch 2956,loss 1359.523682\n",
      "epoch 2957,loss 1355.824829\n",
      "epoch 2958,loss 1354.494263\n",
      "epoch 2959,loss 1349.704590\n",
      "epoch 2960,loss 1345.485596\n",
      "epoch 2961,loss 1348.641113\n",
      "epoch 2962,loss 1340.051147\n",
      "epoch 2963,loss 1335.729614\n",
      "epoch 2964,loss 1335.235352\n",
      "epoch 2965,loss 1332.199463\n",
      "epoch 2966,loss 1326.237183\n",
      "epoch 2967,loss 1317.946533\n",
      "epoch 2968,loss 1317.268677\n",
      "epoch 2969,loss 1317.828003\n",
      "epoch 2970,loss 1313.546997\n",
      "epoch 2971,loss 1306.233521\n",
      "epoch 2972,loss 1303.332275\n",
      "epoch 2973,loss 1304.536865\n",
      "epoch 2974,loss 1293.375977\n",
      "epoch 2975,loss 1288.035767\n",
      "epoch 2976,loss 1289.298462\n",
      "epoch 2977,loss 1286.511230\n",
      "epoch 2978,loss 1277.718872\n",
      "epoch 2979,loss 1275.268066\n",
      "epoch 2980,loss 1270.044312\n",
      "epoch 2981,loss 1266.620728\n",
      "epoch 2982,loss 1262.403564\n",
      "epoch 2983,loss 1257.109863\n",
      "epoch 2984,loss 1256.210327\n",
      "epoch 2985,loss 1252.914795\n",
      "epoch 2986,loss 1252.234985\n",
      "epoch 2987,loss 1247.375000\n",
      "epoch 2988,loss 1243.142212\n",
      "epoch 2989,loss 1238.114380\n",
      "epoch 2990,loss 1238.191406\n",
      "epoch 2991,loss 1235.064209\n",
      "epoch 2992,loss 1230.166992\n",
      "epoch 2993,loss 1222.004883\n",
      "epoch 2994,loss 1219.657959\n",
      "epoch 2995,loss 1221.076050\n",
      "epoch 2996,loss 1221.210083\n",
      "epoch 2997,loss 1225.142090\n",
      "epoch 2998,loss 1204.526733\n",
      "epoch 2999,loss 1203.321045\n",
      "epoch 3000,loss 1204.829956\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "import xlrd\n",
    "import random\n",
    "import math\n",
    "from IPython import display\n",
    "from matplotlib import pyplot as plt\n",
    "from mxnet import autograd, nd\n",
    "batch_size =2\n",
    "num_inputs = 17\n",
    "num_outputs = 1\n",
    "num_hiddens=20\n",
    "\n",
    "\n",
    "w = nd.random.normal(scale=0.001, shape=(num_inputs, num_hiddens))\n",
    "b = nd.zeros(num_hiddens)\n",
    "w1=nd.random.normal(scale=0.0001, shape=(num_hiddens, num_outputs))\n",
    "b1= nd.zeros(num_outputs)\n",
    "\n",
    "w.attach_grad()\n",
    "b.attach_grad()\n",
    "w1.attach_grad()\n",
    "b1.attach_grad()\n",
    "\n",
    "params=[w,b,w1,b1]\n",
    "print(w)\n",
    "def use_svg_display():\n",
    "    # 用矢量图显示\n",
    "    display.set_matplotlib_formats('svg')\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    # 设置图的尺寸\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "\n",
    "def squared_loss(y_hat, y):\n",
    "    return (y_hat - y) ** 2 / batch_size\n",
    "\n",
    "def relu(X):\n",
    "    return nd.maximum(X,0)\n",
    "\n",
    "def net(X):\n",
    "    H=relu(nd.dot(X,w)+b)\n",
    "    Y=nd.dot(H, w1) + b1\n",
    "    return Y\n",
    "\n",
    "def excel2matrix(path):\n",
    "    data = xlrd.open_workbook(path)\n",
    "    table = data.sheets()[0]\n",
    "    nrows = table.nrows  # 行数\n",
    "    ncols = table.ncols  # 列数\n",
    "    datamatrix = nd.random.normal(scale=1,shape=(nrows, ncols))\n",
    "    for i in range(nrows):\n",
    "        rows = table.row_values(i)\n",
    "        datamatrix[i,:] = rows\n",
    "    return datamatrix\n",
    " \n",
    "def data_iter(batch_size, features, labels):\n",
    "    num_examples = len(features)\n",
    "    indices = list(range(num_examples))\n",
    "    random.shuffle(indices)  # 样本的读取顺序是随机的\n",
    "    for i in range(0, num_examples, batch_size):\n",
    "        j = nd.array(indices[i: min(i + batch_size, num_examples)])\n",
    "        yield features.take(j), labels.take(j)  # take函数根据索引返回对应元素\n",
    "# def cross_entropy(y_hat, y):\n",
    "#     return -nd.pick(y_hat, y).log()\n",
    "# def accuracy(y_hat, y):\n",
    "#     return (y_hat.argmax(axis=1) == y.astype('float32')).mean().asscalar()\n",
    "\n",
    "# def evaluate_accuracy(data_iter, net):\n",
    "#     acc_sum, n = 0.0, 0\n",
    "#     for X, y in data_iter:\n",
    "#         y = y.astype('float32')\n",
    "#         acc_sum += (net(X).argmax(axis=1) == y).sum().asscalar()\n",
    "#         n += y.size\n",
    "#     return acc_sum / n\n",
    "\n",
    "num_epochs, lr = 3000, 0.00001\n",
    "\n",
    "def sgd(params, lr, batch_size):  \n",
    "    for param in params:\n",
    "        param[:] = param - lr * param.grad / 2\n",
    "\n",
    "def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,\n",
    "              params=None, lr=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        for X, y in data_iter(batch_size,x,x_label):\n",
    "            with autograd.record():\n",
    "                y_hat = net(X)\n",
    "#                 print('X')\n",
    "#                 print(X)\n",
    "#                 print('y_hat')\n",
    "#                 print(y_hat)\n",
    "#                 print('y')\n",
    "#                 print(y)\n",
    "#                 print('[W,b]')\n",
    "#                 print([w,b])\n",
    "                l = loss(y_hat, y)\n",
    "            l.backward()   #求梯度\n",
    "            sgd(params, lr, batch_size)    #更新wb权重   \n",
    "#         print(params)\n",
    "        train_l_sum =loss(net(x),x_label)  #误差\n",
    "        print('epoch %d,loss %f' % (epoch + 1,train_l_sum.mean().asnumpy()))\n",
    "\n",
    "\n",
    "pathX = '标准化tower1.xls'  #  113.xlsx 在当前文件夹下\n",
    "pathX2 = 'tower1label.xls'  #  113.xlsx 在当前文件夹下\n",
    "pathX3 = '标准化tower2.xls'  #  113.xlsx 在当前文件夹下\n",
    "x = excel2matrix(pathX)\n",
    "x_label=excel2matrix(pathX2)\n",
    "y_test=excel2matrix(pathX3)\n",
    "y_label=nd.zeros((y_test.shape[0],1))\n",
    "\n",
    "train_iter=data_iter(batch_size,x,x_label)\n",
    "test_iter=data_iter(batch_size,y_test,y_label)\n",
    "train_ch3(net, train_iter, test_iter, squared_loss, num_epochs, batch_size,params, lr)\n",
    "#set_figsize()\n",
    "#plt.scatter(x[:, 1].asnumpy(), x_label[:, 0].asnumpy(), 1);  # 加分号只显示图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e86025e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[[-0.6141013  -1.2764623   0.5353347   0.95070267  1.2393179   1.1050532\n",
      "   1.7507896  -1.915077   -1.4111595  -0.21031138  0.60091853  1.574448\n",
      "   1.0515065   0.32601175  0.26119736  0.22392258  0.5353347 ]\n",
      " [-1.265895   -1.2764623   0.57450354 -0.04375124  0.6031531   0.46555597\n",
      "   0.8927713   0.8664862   0.38962168  0.5566916   0.7859604   0.660901\n",
      "   0.59752434  1.3908843   0.78824925  1.7774764   0.57450354]\n",
      " [-0.8048198  -1.1250309  -0.43043426 -0.72958153  0.01309746  0.110312\n",
      "   0.186567    0.5943349  -0.42209148  0.6138888   0.6928498   0.4106147\n",
      "   0.21125947  0.8728382   0.7717789   0.8344706  -0.43043426]\n",
      " [ 0.27081615  0.8435774  -0.11985309 -0.72958153 -2.003515   -2.004273\n",
      "  -0.85378766  0.25252622  1.0266211  -1.296526   -0.99899757  0.04832463\n",
      "   0.6263399   0.8728382   1.6117678   0.09005492 -0.11985309]\n",
      " [ 1.5382924   1.449303   -0.06446282 -0.3866664  -0.5654751  -0.55952996\n",
      "  -0.8617674   0.3465737   0.19308403 -0.1190768  -0.57068723 -0.92715275\n",
      "  -0.73080045  1.7650286   1.9411752   1.3225725  -0.06446282]\n",
      " [ 0.21425904  1.2221559   0.42613673 -0.21520881  1.4378875   1.3310592\n",
      "   1.2169471   0.01558387  1.4366306  -0.16171008  0.02638411  0.33205837\n",
      "   0.34891477  0.2972314   0.16237514  0.259293    0.42613673]\n",
      " [ 0.10983448  0.08642033 -0.10204908 -0.04375124  0.6013865   0.62935656\n",
      "   1.2897619   0.97901064 -1.5688648   1.842091    2.4374056   1.5443548\n",
      "   1.0573393   0.21089041  0.11296403  0.23387967 -0.10204908]\n",
      " [ 1.3938133  -0.595021   -0.21876429  0.98499423 -0.4400441  -0.3995926\n",
      "  -0.4707616   0.8259504  -1.166334    1.0680934   0.8669102   0.08779848\n",
      "  -0.29063895 -0.9115428  -0.57879156 -1.0785663  -0.21876429]\n",
      " [ 1.8322567  -0.36787388  0.4973528  -0.04375124 -0.5089428  -0.61886674\n",
      "  -0.44283262  0.7163863  -0.8137376   0.72698164  0.56452584  0.02557856\n",
      "  -0.19896801  0.09576906 -0.64467305  1.0370792   0.4973528 ]\n",
      " [ 1.5297946   0.69214594 -0.19898206 -0.11233427 -1.1908635  -1.1663159\n",
      "  -1.448276    0.14048904  1.0357827  -0.93160665 -1.5421396  -1.8557962\n",
      "  -1.135257   -0.27837533 -0.39761746  0.09616579 -0.19898206]\n",
      " [-0.71803176 -0.36787388 -1.2292411  -0.8324561   1.1050538   1.4531578\n",
      "   0.26257372 -1.8649739  -0.04424689 -0.83053756 -0.72495407 -0.15265523\n",
      "  -0.44908294  0.09576906  0.14590476 -0.09584051 -1.2292411 ]\n",
      " [ 0.68666786 -1.2007467  -2.0106397   0.67637056  0.24381982  0.7534144\n",
      "  -0.610207   -1.566639   -0.803246   -0.5180353  -0.6922967  -0.5915694\n",
      "  -0.9333323  -0.6525197  -0.08468042 -1.2189202  -2.0106397 ]\n",
      " [-0.71933234 -1.2764623  -1.0104495  -0.3866664   0.5314278   0.79101413\n",
      "  -1.2629473   0.2031966   0.8303046  -0.08892781 -1.2721369  -2.361274\n",
      "  -1.8596575  -0.16325398 -0.52938044  0.23044156 -1.0104495 ]\n",
      " [-0.57630116 -0.6707367  -0.6697994   0.2991639   0.9838627   1.1691152\n",
      "  -0.23336524  1.0535077  -0.5930666   1.3677309   0.7404447  -0.49099106\n",
      "  -0.9197869   0.13893956  0.54119366 -0.45214102 -0.6697994 ]\n",
      " [ 0.11388399  0.427141   -0.9190556   1.3279094  -0.5760749  -0.38625982\n",
      "  -1.0572703   0.19726177  1.1623105  -0.67808825 -1.2392579  -1.537308\n",
      "  -0.993001    0.9879595   0.44237146  1.5032893  -0.9190556 ]\n",
      " [ 1.7404906   0.01070463 -0.94675076 -1.758327   -0.8534364  -0.6692029\n",
      "  -0.39295945  0.3465737  -0.19999127 -0.02913413  0.02120793  0.11276967\n",
      "   0.16242774 -0.82520175 -0.7187897  -0.7147356  -0.94675076]\n",
      " [-1.4456592  -0.8221681  -1.2941269   0.2991639  -0.46089038 -0.18041836\n",
      "  -0.00953438 -0.26576373 -0.02951594 -0.32310185 -0.10069932  0.33705163\n",
      "   0.3499455   1.0743005   1.1917733   0.7989959  -1.2941269 ]\n",
      " [ 0.85180706 -0.4435896  -0.87355644  0.98499423  0.02723053  0.23086518\n",
      "  -0.4956982  -1.9072976  -0.5797758  -0.9134834  -0.9055209  -0.4216953\n",
      "  -0.6558446   0.03820838  0.12943439 -0.07192306 -0.87355644]\n",
      " [ 0.5959075   0.54071456 -1.0001628   2.0823226   0.53602105  0.79309464\n",
      "   3.13108    -1.4876317  -1.7737737   0.14401422  1.541766    2.7607074\n",
      "   3.2587845  -0.19203432 -0.29879525 -0.19681405 -1.0001628 ]\n",
      " [ 1.5226783   0.01070463 -1.5520873  -1.0724967  -2.0247145  -1.7855228\n",
      "  -0.8617674   0.12736335  1.4172955  -1.5382477  -1.2109742  -0.01315654\n",
      "   0.6380063  -0.249595    0.12943439 -0.5454782  -1.5520873 ]\n",
      " [-0.9758736  -0.06501107  0.82969445  0.2991639   0.9114307   0.70739114\n",
      "   1.520176    0.2894849   0.8439112   0.08891603  0.5862571   1.0240937\n",
      "   1.0614095  -0.07691297 -0.03526931 -0.19706741  0.82969445]\n",
      " [-1.1406611   1.2221559   0.9444314  -0.3866664   2.057976    1.799638\n",
      "   0.28132606  0.8573505  -1.1269855   1.8717545   1.405476   -0.0478103\n",
      "  -0.9352408  -1.5159298  -1.5176027  -1.4558764   0.9444314 ]\n",
      " [-1.1268942   1.6007344   1.1066458   1.3279094   1.1940922   0.91480917\n",
      "   0.32321954  1.0535077  -0.5930666   1.436221    1.143279    0.06930897\n",
      "  -0.4366823  -1.1705658  -0.8093768  -1.4968152   1.1066458 ]\n",
      " [-0.5904849  -0.9735995   1.1422539   0.4706215  -0.5919746  -0.8278732\n",
      "  -0.01791307 -1.9754318   1.3169904  -1.9104685  -1.4551933   0.11080371\n",
      "   0.45558897  1.3621039   0.9200122   1.6945993   1.1422539 ]\n",
      " [-0.8194094   0.40442628  1.0116911  -0.04375124 -1.4629251  -1.6517713\n",
      "  -0.27525872  0.28932032  0.55150366 -0.7580713  -0.32913     0.6061434\n",
      "   0.9677936  -1.8037332  -2.3905323  -0.6492292   1.0116911 ]\n",
      " [-0.4930283   0.5558577   1.4033794   0.64207906  0.2666094  -0.0512782\n",
      "  -0.0857406   0.22700039  1.1047274  -0.2959762  -0.4868121  -0.5008609\n",
      "  -0.31183195 -1.8900743  -1.5176027  -2.006272    1.4033794 ]\n",
      " [-0.7246578   2.6607542   1.4567915  -3.1299877  -0.9285183  -1.2110733\n",
      "  -0.33809894  1.2212168  -0.00345362  0.33403325  0.34912407  0.19270457\n",
      "   0.31146172 -1.8037332  -1.6658361  -1.5507234   1.4567915 ]\n",
      " [-0.6830229   0.01070463  1.0931939   0.2991639  -0.4902165  -0.719487\n",
      "  -0.07975583 -0.57193244  1.2861985  -1.1097925  -0.8939952  -0.04807878\n",
      "   0.28020215  0.64259547  0.36166662  0.9044851   1.0931939 ]\n",
      " [ 0.29767108 -1.2764623   1.6190058  -0.72958153  0.3452246  -0.02237141\n",
      "  -1.0572703   0.951622   -1.4656725   1.662679    0.6602857  -0.949313\n",
      "  -1.5283793   1.3621039   1.6776493   0.7236769   1.6190058 ]\n",
      " [-1.0800453  -1.5547849   0.81863886 -0.76069826  0.77666974  0.66124386\n",
      "  -0.51286435 -1.887919   -0.69583154 -0.9317875  -1.440184   -0.54307735\n",
      "  -0.6279673  -0.5106003   0.8212561  -1.7036959   0.81863886]\n",
      " [ 0.12424933 -0.80389446 -0.08750789 -1.188591    0.50280875  0.49553612\n",
      "  -1.6016383   1.0043342  -1.2268384   1.6691526   1.313271   -1.5262843\n",
      "  -1.4387612   0.54063565  0.8212561  -0.16326185 -0.08750789]\n",
      " [-0.9487521   0.60954636  1.3882169   1.188591   -1.5237626  -1.6170926\n",
      "   1.3259538   0.17469221  1.7297417  -1.0570313  -0.7203591   1.4847425\n",
      "   1.5464728   0.31537077 -0.11911348  0.7559934   1.3882169 ]\n",
      " [ 0.20921226  0.7420564  -1.3328124  -0.42789277 -0.82450414 -0.66322714\n",
      "   0.16770574  0.0849159   0.0905787  -0.13790272  0.02155584  0.30160397\n",
      "   0.3261273  -1.6369245  -1.87447    -0.10864087 -1.3328124 ]\n",
      " [ 1.6953359   1.0070766  -0.7865354   1.188591    1.0687882   1.1235398\n",
      "   0.6208431   0.6239766   0.1023495   0.45756885  0.8257162   0.28301516\n",
      "   0.19412851  1.2915184   0.3510713   1.2196052  -0.7865354 ]]\n",
      "<NDArray 34x17 @cpu(0)>\n"
     ]
    }
   ],
   "source": [
    "y_test=excel2matrix(pathX3)\n",
    "a=net(y_test)\n",
    "print(y_test)\n",
    "#set_figsize()\n",
    "#plt.scatter(x[:, 1].asnumpy(), x_label[:, 0].asnumpy(), 1);  # 加分号只显示图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "77210598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.1091547e+02]\n",
      " [7.1501056e+02]\n",
      " [2.1719536e+03]\n",
      " [2.3167154e+02]\n",
      " [6.0754828e+02]\n",
      " [1.5536169e+03]\n",
      " [8.7161627e+02]\n",
      " [6.9948358e+02]\n",
      " [1.0192263e+03]\n",
      " [2.6977124e+02]\n",
      " [5.1259668e+02]\n",
      " [4.4362701e+02]\n",
      " [2.0110780e+02]\n",
      " [7.2506696e+02]\n",
      " [3.1494501e+02]\n",
      " [5.0817792e+02]\n",
      " [1.7940872e+03]\n",
      " [1.3702988e+03]\n",
      " [1.6162258e+03]\n",
      " [1.9059009e+02]\n",
      " [4.7708097e+00]\n",
      " [8.9043225e+02]\n",
      " [9.3142853e+02]\n",
      " [7.8515985e+02]\n",
      " [1.3194961e+03]\n",
      " [2.3583200e+02]\n",
      " [6.5048774e+03]\n",
      " [4.9081369e+02]\n",
      " [4.7708097e+00]\n",
      " [5.8840328e+01]\n",
      " [1.7698320e+01]\n",
      " [1.0923475e+03]\n",
      " [1.9401562e+03]\n",
      " [6.6043378e+02]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a1=a.asnumpy()\n",
    "print(a1)\n",
    "np.savetxt(\"./result.txt\",a1,fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05b4f098",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python [conda env:gluon] *",
   "language": "python",
   "name": "conda-env-gluon-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "mimetype": "text/x-python",
   "name": "python",
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